Bibliographic record
Abstract
As a major sink for anthropogenic carbon, the oceans slow the increase of carbon dioxide in the atmosphere and regulate climate change. Future changes in the ocean carbon sink, and its uncertainty at a global and regional scale, are key to understanding the future evolution of the climate. Here, we conduct a multimodel analysis of the changes and uncertainties in the historical and future ocean carbon sink using output data from the latest phase of the Coupled Model Intercomparison Project: CMIP6, and observations. We show that the ocean carbon sink is concentrated in highly active regions – 70 percent of the total sink occurs in less than 40 percent of the global ocean. High pattern correlations between the historical and projected future carbon sink indicate that future uptake will largely continue to occur in historically important regions. We conduct a detailed breakdown of the sources of uncertainty in the future carbon sink by region. Scenario uncertainty dominates at the global scale, followed by model uncertainty, and then internal variability. We demonstrate how the importance of internal variability increases moving to smaller spatial scales and go on to show how the breakdown between scenario, model, and internal variability changes between different ocean basins, governed by different processes. Moreover, we show that internal variability changes with time based on the scenario. As with the mean sink, we show that uncertainty in the future ocean carbon sink is also concentrated in the known regions of historical uptake. The resulting patterns in the signal-to-noise ratio have strong implications for observational detectability and time of emergence, which varies both in space and with scenario. Our results suggest that to detect human influence on the ocean carbon sink as early as possible and to efficiently reduce uncertainty in future carbon uptake, modeling and observational efforts should be focused in the known regions of high historical uptake, including the Northwest Atlantic and the Southern Ocean.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.032 | 0.018 |
| Insufficient payload (model declined to judge) | 0.044 | 0.038 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".